Design a data architecture that accelerates data readiness for generative AI and unlock unparalleled productivity for data teams.
Fragmented data stacks, productivity pressures and the lack of data preparedness for generative AI are driving enterprises to evaluate new data strategies. Data fabric is designed to bring the power of generative AI to streamline the integration, curation, governance and delivery of high-quality data for analytics and artificial intelligence (AI).
The next-generation data fabric is hybrid by design and can run anywhere, on-premises or in any cloud environment. It also integrates across hybrid data planes, supporting any style of data integration.
Data fabric introduces new data intelligence and integration tools to prepare data for generative AI, helping to ensure readiness for both structured and unstructured data in AI initiatives. By streamlining data preparation and integration, organizations can unlock productivity for their data teams and drive business innovation.
Read the guide to building a data-driven organization
Connect data from disparate sources in multicloud environments with a range of integration styles including batch, real-time and change data capture.
Use large language models (LLMs) to scale contextual understanding of data, empowering data consumers to trust and use reliable information.
Key features of a data fabric architecture
An abstraction layer that provides a common business understanding of the data processing and automation to act on insights.
A range of integration styles to extract, ingest, stream, virtualize and transform unstructured data, driven by data policies to maximize performance while minimizing storage and costs.
A marketplace that supports self-service consumption, enabling users to find, collaborate and access high-quality data.
End-to-end lifecycle management for composing, building, testing, optimization and deploying various capabilities of a data fabric architecture.
Unified definition and enforcement of data policies, data governance, data security and data stewardship for a business-ready data pipeline.
An AI-infused composable architecture built for hybrid cloud environments.
A data fabric is an architectural approach designed to simplify data access and facilitate self-service data consumption for an organization's unique workflow. End-to-end data fabric capabilities include data matching, observability, master data management, data quality, real-time data integration and more, all of which can be implemented without replacing existing tech stacks.
Whether simplifying the day-to-day work for data producers or providing self-service data access to data engineers, data scientists and business users, a data fabric prepares and delivers the information needed for better insights and decision-making.
A strong data foundation is critical for the success of AI implementations.
With a unified data and AI platform, the IBM Global Chief Data Office increased its business pipeline by USD 5 billion in 3 years.
Luxembourg Institute of Science and Technology built a state-of-the-art platform with faster data delivery to empower companies and researchers.
State Bank of India transformed its customer experience by designing an intelligent platform with faster, more secured data integration.
Frequently asked questions
A data fabric and data mesh can coexist. A data fabric provides the capabilities needed to implement and take full advantage of a data mesh by automating many of the tasks required to create data products and manage the lifecycle of data products. By using the flexibility of a data fabric foundation, you can implement a data mesh, continuing to take advantage of a use case-centric data architecture regardless of whether your data resides on-premises or in the cloud.
Read: Three ways a data fabric enables the implementation of a data mesh
Data virtualization is one of the technologies that enables a data fabric approach. Rather than physically moving the data from various on-premises and cloud sources by using the standard extract, transform, load (ETL) process, a data virtualization tool connects to different data sources, integrates only the metadata required and creates a virtual data layer. This allows users to use the source data in real time.
Data continues to compound and is often too difficult for organizations to access information. This data holds unseen insights, which result in a knowledge gap.
With data virtualization capabilities in a data fabric architecture, organizations can access data at the source without moving it, helping to accelerate time to value through faster, more accurate queries.
Data management tools started with databases and evolved to data warehouses and data lakes across clouds and on-premises as more complex business problems emerged. But enterprises are consistently constrained by running workloads in performance and cost-inefficient data warehouses and lakes and are inhibited by their ability to run analytics and AI use cases.
The advent of new, open source technologies and the desire to reduce data duplication and complex ETL pipelines is resulting in a new architectural approach known as the data lakehouse, which offers the flexibility of a data lake with the performance and structure of a data warehouse, along with shared metadata and built-in governance, access controls and security.
However, to access all of this data that is now optimized and locally governed by the lakehouse across your organization, a data fabric is required to simplify data management and enforce access globally. A data fabric helps you optimize your data’s potential, foster data sharing and accelerate data initiatives by automating data integration, embedding governance and facilitating self-service data consumption in a way that storage repositories cannot.
A data fabric is the next step in the evolution of these tools. With this architecture, you can continue to use the disparate data storage repositories you’ve invested in while simplifying data management.